video_prediction | Stochastic Adversarial Video Prediction | Video Utils library
kandi X-RAY | video_prediction Summary
kandi X-RAY | video_prediction Summary
video_prediction is a Python library typically used in Video, Video Utils, Pytorch, Generative adversarial networks applications. video_prediction has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Stochastic Adversarial Video Prediction
Stochastic Adversarial Video Prediction
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video_prediction has a low active ecosystem.
It has 286 star(s) with 64 fork(s). There are 16 watchers for this library.
It had no major release in the last 6 months.
There are 24 open issues and 23 have been closed. On average issues are closed in 16 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of video_prediction is current.
Quality
video_prediction has 0 bugs and 0 code smells.
Security
video_prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
video_prediction code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
video_prediction is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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video_prediction releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
video_prediction saves you 3075 person hours of effort in developing the same functionality from scratch.
It has 6625 lines of code, 288 functions and 34 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed video_prediction and discovered the below as its top functions. This is intended to give you an instant insight into video_prediction implemented functionality, and help decide if they suit your requirements.
- Call the model
- Pad 2d array with padding
- Pad inputs into a 2d tensor
- 2d convolution layer
- Build the graph
- Transpose a tensor
- Compute the tower function
- Evaluate outputs and metrics
- Parse a tf example
- Parse the given example
- Upsample a convolutional convolution layer
- Parse a serialized example
- Create a tf Summary for a gif summary
- 2D discriminator layer
- Saves images using ffmpeg
- Save prediction results
- Read frames and save them as tf records
- Convolutional layer
- Image discriminator
- 2D convolutional convolution layer
- A 2D convolutional convolution layer
- Kronecker local version of kronecker
- Calculate separable separable separable
- Upsample a convolution layer
- Generate a generator function
- Local 2D layer
Get all kandi verified functions for this library.
video_prediction Key Features
No Key Features are available at this moment for video_prediction.
video_prediction Examples and Code Snippets
No Code Snippets are available at this moment for video_prediction.
Community Discussions
Trending Discussions on video_prediction
QUESTION
Imagemagick convert low quality gif after chop
Asked 2019-Jun-13 at 20:55
I'm trying to chop the following gif as to remove four of the inner videos:
I use this command:
convert bair_action_free_00232.gif -gravity NorthWest -chop 256x0+65+0 vid_pred.gif
The result looks really bad; the black frame flickers and the text looks awful.
Adding -quality 99 doesn't help. Any tips?
Gif Reference: https://alexlee-gk.github.io/video_prediction/
...ANSWER
Answered 2019-Jun-13 at 20:55I think you want this:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install video_prediction
Clone this repo:
Install TensorFlow >= 1.9 and dependencies from http://tensorflow.org/
Install ffmpeg (optional, used to generate GIFs for visualization, e.g. in TensorBoard)
Install other dependencies
In python >= 3.6, make sure to add the root directory to the PYTHONPATH, e.g. export PYTHONPATH=path/to/video_prediction.
For the best speed and experimental results, we recommend using cudnn version 7.3.0.29 and any tensorflow version >= 1.9 and <= 1.12. The final training loss is worse when using cudnn versions 7.3.1.20 or 7.4.1.5, compared to when using versions 7.3.0.29 and below.
In macOS, make sure that bash >= 4.0 is used (needed for associative arrays in download_model.sh script).
Install TensorFlow >= 1.9 and dependencies from http://tensorflow.org/
Install ffmpeg (optional, used to generate GIFs for visualization, e.g. in TensorBoard)
Install other dependencies
In python >= 3.6, make sure to add the root directory to the PYTHONPATH, e.g. export PYTHONPATH=path/to/video_prediction.
For the best speed and experimental results, we recommend using cudnn version 7.3.0.29 and any tensorflow version >= 1.9 and <= 1.12. The final training loss is worse when using cudnn versions 7.3.1.20 or 7.4.1.5, compared to when using versions 7.3.0.29 and below.
In macOS, make sure that bash >= 4.0 is used (needed for associative arrays in download_model.sh script).
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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